In:
Annals of the Rheumatic Diseases, BMJ, Vol. 81, No. Suppl 1 ( 2022-06), p. 70.2-71
Kurzfassung:
Rheumatoid arthritis (RA) is a complex disease, caused by a combination of genetic, epigenetic and environmental factors common to other related autoimmune diseases including Multiple Sclerosis (MS) and Systemic Lupus Erythematosus (SLE) [1]. Using state of the art Bioinformatics tools we are able to formulate an ensemble of associated components (genomic grammar) for each disease and distinguish important differences and common aspects in a specific group of disease such as ensembles of autoimmune diseases [2] . Objectives To create, collect and evaluate the most credible and unique gene variants, epigenetic variants and single nucleotide polymorphisms (SNPs) causing the basis of an immune disease (the genomic grammar of the disease), which could potentially assist in the process of the RA disease prevention, diagnosis and treatment [3]. Methods RA related publications from the PubMed have been analyzed using data mining and semantic techniques towards extracting the candidate causative SNPs. The extracted knowledge has been filtered, evaluated, annotated, and classified in a structured database which also includes GWAS information regarding SNPs. Additional clinical, genomic, structural, functional and biological information was also extracted from biological databases including dbSNP, LitVar, ClinVar and OMIM and cross-correlated with other available autoimmune disease related SNP databases, including the Demetra application, Epione application and Panacea application databases [3, 4]. Results A holistic genetic map of the studied autoimmune diseases with more than 2000 related SNPs has been estimated and specific sub-clusters with crucial nodes have been identified across the RA, SLE and MS diseases. Based on these results, the three studied autoimmune diseases share a 10% common SNPs genetic background (Figure 1 and Table 1) [5]. The optimal genomic grammar of the RA contains 1682 SNPs, with 73% responding to non-coding regions and 27% responding to coding regions of more than 1.300 genes, pseudogenes, primers and promoters. RA also shares 464 common SNPs with SLE and 113 with MS. Table 1. Common Related Genes based on the analyzed SNP targets in the studied disease. A/A Gene / Region A/A Gene / Region 1. ADAM33 2. LOC285626 3. ADIPOQ 4. MIR3142HG 5. CD40 6. MIR499A 7. CIITA 8. MTHFR 9. CTLA4 10. MT-ND5 11. FCRL3 12. NCF1 13. HLA-DPB1 14. NLRP1 15. HLA-DRA 16. NOS3 17. HLA-G 18. NR3C1 19. IL17A 20. PADI4 21. IL1RN 22. PDCD1 23. IL2 24. PON1 25. IL23R 26. STAT4 27. IL6 28. TGFB1 29. IL7R 30. TLR9 31. IRAK1 32. TNF 33. VDR 34. TNFRSF1A 35. IRF5 36. TYK2 37. KIF5A 38. UCP2 39. LEP Figure 1. Three class Venn diagram of the genomic grammar between RA, MS and SLE. Conclusion The identification of the optimal genomic grammar in RA will help towards understanding the nature of the disease. Specific genetic targets via determined SNPs could act as biomarkers that aid in forming the right diagnosis [6]. References [1]Acosta-Herrera et al, J Clin Med 2019;8:826 [2]Chatzikyriakidou et al, Semin Arthritis Rheum 2013;43:29 [3]Papageorgiou et al, Int J Mol Med 2021;47:115 [4]Papageorgiou et al, Int J Mol Med 2022;49:8 [5]Wang, Y et al, Ann Rheum Dis 2021Epub ahead of print:doi:10.1136/annrheumdis-2021-220066 [6]Kurko et al, Clin Rev Allergy Immunol 2013;45:170 Disclosure of Interests None declared
Materialart:
Online-Ressource
ISSN:
0003-4967
,
1468-2060
DOI:
10.1136/annrheumdis-2022-eular.2147
Sprache:
Englisch
Verlag:
BMJ
Publikationsdatum:
2022
ZDB Id:
1481557-6
